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@ -1630,12 +1630,12 @@
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"metadata": {
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"metadata": {
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"id": "MY5faq4yLdpQ",
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"outputId": "c3838b07-0d15-471e-8dad-370de91d4bdc",
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"colab": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"base_uri": "https://localhost:8080/",
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"height": 204
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"height": 204
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}
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},
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"id": "MY5faq4yLdpQ",
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"outputId": "c3838b07-0d15-471e-8dad-370de91d4bdc"
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},
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},
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"source": [
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"source": [
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"fill_with_mode = pd.DataFrame([[1,2,\"True\"],\n",
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"fill_with_mode = pd.DataFrame([[1,2,\"True\"],\n",
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@ -1736,11 +1736,11 @@
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"metadata": {
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"metadata": {
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"id": "WKy-9Y2tN5jv",
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"outputId": "41f5064e-502d-4aec-dc2d-86f885068b4f",
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"colab": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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"base_uri": "https://localhost:8080/"
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}
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},
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"id": "WKy-9Y2tN5jv",
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"outputId": "41f5064e-502d-4aec-dc2d-86f885068b4f"
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},
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},
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"source": [
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"source": [
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"fill_with_mode[2].value_counts()"
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"fill_with_mode[2].value_counts()"
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@ -1784,12 +1784,12 @@
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"metadata": {
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"metadata": {
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"id": "tvas7c9_OPWE",
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"outputId": "7282c4f7-0e59-4398-b4f2-5919baf61164",
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"colab": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"base_uri": "https://localhost:8080/",
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"height": 204
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"height": 204
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}
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},
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"id": "tvas7c9_OPWE",
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"outputId": "7282c4f7-0e59-4398-b4f2-5919baf61164"
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},
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},
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"source": [
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"source": [
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"fill_with_mode"
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"fill_with_mode"
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@ -1894,19 +1894,252 @@
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"\n",
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"\n",
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"We replace with Median, in case of skewed data with outliers. This is beacuse median is robust to outliers.\n",
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"We replace with Median, in case of skewed data with outliers. This is beacuse median is robust to outliers.\n",
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"\n",
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"\n",
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"When the data is normalized, we can use mean, as in that case, mean and median would be pretty close."
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"When the data is normalized, we can use mean, as in that case, mean and median would be pretty close.\n",
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"\n",
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"First, let us take a column which is normally distributed and let us fill the missing value with the mean of the column. "
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]
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]
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},
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},
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{
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{
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"cell_type": "code",
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"cell_type": "code",
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"metadata": {
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"metadata": {
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"id": "09HM_2feOj5Y"
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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},
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"id": "09HM_2feOj5Y",
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"outputId": "ade42fec-dc40-45d0-e22c-974849ea8664"
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},
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},
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"source": [
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"source": [
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""
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"fill_with_mean = pd.DataFrame([[-2,0,1],\n",
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" [-1,2,3],\n",
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" [np.nan,4,5],\n",
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" [1,6,7],\n",
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" [2,8,9]])\n",
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"\n",
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"fill_with_mean"
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],
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],
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"execution_count": null,
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"execution_count": 33,
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"outputs": []
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>-2.0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>-1.0</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>NaN</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1.0</td>\n",
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" <td>6</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2.0</td>\n",
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" <td>8</td>\n",
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" <td>9</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1 2\n",
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"0 -2.0 0 1\n",
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"1 -1.0 2 3\n",
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"2 NaN 4 5\n",
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"3 1.0 6 7\n",
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"4 2.0 8 9"
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]
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},
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"metadata": {},
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"execution_count": 33
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}
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "ka7-wNfzSxbx"
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},
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"source": [
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"The mean of the column is"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "XYtYEf5BSxFL",
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"outputId": "1e79aeea-6baf-4572-dcd1-23e5ec742036",
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"colab": {
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"base_uri": "https://localhost:8080/"
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}
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},
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"source": [
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"np.mean(fill_with_mean[0])"
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],
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"execution_count": 34,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0.0"
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]
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},
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"metadata": {},
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"execution_count": 34
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "oBSRGxKRS39K"
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},
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"source": [
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"Filling with mean"
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "FzncQLmuS5jh",
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"outputId": "75f33b25-e6b3-41bb-8049-1ed2e085efe2",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 204
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}
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},
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"source": [
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"fill_with_mean[0].fillna(np.mean(fill_with_mean[0]),inplace=True)\n",
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"fill_with_mean"
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],
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"execution_count": 35,
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th>0</th>\n",
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" <th>1</th>\n",
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" <th>2</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
|
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" <td>-2.0</td>\n",
|
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" <td>0</td>\n",
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" <td>1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>-1.0</td>\n",
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" <td>2</td>\n",
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" <td>3</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>0.0</td>\n",
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" <td>4</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>1.0</td>\n",
|
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|
" <td>6</td>\n",
|
|
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|
" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2.0</td>\n",
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" <td>8</td>\n",
|
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|
" <td>9</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 0 1 2\n",
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"0 -2.0 0 1\n",
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"1 -1.0 2 3\n",
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"2 0.0 4 5\n",
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"3 1.0 6 7\n",
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"4 2.0 8 9"
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]
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},
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"metadata": {},
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"execution_count": 35
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "CwpVFCrPTC5z"
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},
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"source": [
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|
"As we can see, the missing value has been replaced with its mean."
|
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|
|
|
|
]
|
|
|
|
},
|
|
|
|
},
|
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|
{
|
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{
|
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"cell_type": "code",
|
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"cell_type": "code",
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|